Dataset:
We used the same training dataset as [1]}. This dataset includes 4,249 hours of clean speech which consists of 281k utterances from the LibriSpeech [2]} training set and 1,916k utterances from an internal, vendor collected dataset. The noisy utterances were generated using a room simulator [3]}, with SNR from -10 dB to 30 dB. Noise is sampled from internally collected noise snippets that simulate conditions like cafe, kitchen, and cars, and freely available noise sources from Getty [4]} and YouTube Audio Library [5]}. Room configurations have reverberation times (T60) ranging from 0 ms to 900 ms. We generated multiple copies of the data under different mixing conditions in order to model enough combinations of clean speech, background noise, and room-configuration. As \(\mathbf {s}\)  and \(\mathbf {n}\) , we used reverberated speech and noise, respectively. The training dataset includes 39.6M noisy samples (55,027 hours) in total.
